It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integr...It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.展开更多
Purpose:This study explores how search motivation and context influence mobile Web search behaviors.Design/methodology/approach:We studied 30 experienced mobile Web users via questionnaires,semi-structured interviews,...Purpose:This study explores how search motivation and context influence mobile Web search behaviors.Design/methodology/approach:We studied 30 experienced mobile Web users via questionnaires,semi-structured interviews,and an online diary tool that participants used to record their daily search activities.SQLite Developer was used to extract data from the users' phone logs for correlation analysis in Statistical Product and Service Solutions(SPSS).Findings:One quarter of mobile search sessions were driven by two or more search motivations.It was especially difficult to distinguish curiosity from time killing in particular user reporting.Multi-dimensional contexts and motivations influenced mobile search behaviors,and among the context dimensions,gender,place,activities they engaged in while searching,task importance,portal,and interpersonal relations(whether accompanied or alone when searching) correlated with each other.Research limitations:The sample was comprised entirely of college students,so our findings may not generalize to other populations.More participants and longer experimental duration will improve the accuracy and objectivity of the research.Practical implications:Motivation analysis and search context recognition can help mobile service providers design applications and services for particular mobile contexts and usages.Originality/value:Most current research focuses on specific contexts,such as studies on place,or other contextual influences on mobile search,and lacks a systematic analysis of mobile search context.Based on analysis of the impact of mobile search motivations and search context on search behaviors,we built a multi-dimensional model of mobile search behaviors.展开更多
基金support from Shenzhen Municipal Development and Reform Commission(Grant Number:SDRC[2016]172)Shenzhen Science and Technology Program(Grant No.KQTD20170810150821146)Interdisciplinary Research and Innovation Fund of Tsinghua Shenzhen International Graduate School,and Shanghai Shun Feng Machinery Co.,Ltd.
文摘It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.
基金supported by the Wuhan International Science and Technology Cooperation Fund (Grant No.:2015030809020371)the Wuhan University Youth Fund of Humanities and Social Sciences
文摘Purpose:This study explores how search motivation and context influence mobile Web search behaviors.Design/methodology/approach:We studied 30 experienced mobile Web users via questionnaires,semi-structured interviews,and an online diary tool that participants used to record their daily search activities.SQLite Developer was used to extract data from the users' phone logs for correlation analysis in Statistical Product and Service Solutions(SPSS).Findings:One quarter of mobile search sessions were driven by two or more search motivations.It was especially difficult to distinguish curiosity from time killing in particular user reporting.Multi-dimensional contexts and motivations influenced mobile search behaviors,and among the context dimensions,gender,place,activities they engaged in while searching,task importance,portal,and interpersonal relations(whether accompanied or alone when searching) correlated with each other.Research limitations:The sample was comprised entirely of college students,so our findings may not generalize to other populations.More participants and longer experimental duration will improve the accuracy and objectivity of the research.Practical implications:Motivation analysis and search context recognition can help mobile service providers design applications and services for particular mobile contexts and usages.Originality/value:Most current research focuses on specific contexts,such as studies on place,or other contextual influences on mobile search,and lacks a systematic analysis of mobile search context.Based on analysis of the impact of mobile search motivations and search context on search behaviors,we built a multi-dimensional model of mobile search behaviors.
